Dissertation > Excellent graduate degree dissertation topics show

Microcalcification Clusters Detection Algorithms Based on SVM in Mammograms

Author: SuXiaoJuan
Tutor: LiuYingJie
School: Lanzhou University
Course: Circuits and Systems
Keywords: Computer-aided detection Microcalcifications detection Nonsubsampled Contourlet Transform Support Vector Machine
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 34
Quote: 0
Read: Download Dissertation

Abstract


Made a thorough study microcalcification cluster detection of breast images is proposed based on the nonsubsampled Contourlet transform and support vector machine microcalcifications cluster detection algorithm , and effectively improve the correct rate of detection of microcalcifications clusters . The X-ray image of the breast is an effective tool for the detection of breast cancer , the breast image microcalcifications is an important sign of early breast cancer , a result the microcalcifications detection of breast images have a very important role in the early diagnosis of breast cancer . This study mainly the following aspects: (1) for the breast X-ray images with low contrast , proposes a nonsubsampled Contourlet transform breast X image enhancement method , this method can effectively suppress noise and can easily cause interference highlighting the linear structure , while enhancing the calcifications . ( 2 ) In order to reduce the the microcalcification detection of false positive rate , Nonsubsampled Contourlet transform domain image to extract effective features as the input of the SVM classifier . The experiments show that compared with the wavelet transform , a better representation of image features can be extracted in Nonsubsampled Contourlet transform domain image information . (3 ) In order to detect the breast image is normal , as well as the severity of the abnormality is benign or malignant (ie, whether the microcalcifications clusters ) , this article uses two SVM classifier . The first level of classification determines breast image is normal that the presence of microcalcifications , if normal output , abnormal by the second level classifier to determine its severity . The experiments show that better practicability of the proposed algorithm with respect to the traditional algorithms can effectively detect abnormal breast X images and classify its severity , and provide a new method for computer-aided detection of breast cancer .

Related Dissertations

  1. Research on Automatic Detection Algorithm for Substructure Distress of Highway Pavement Based on SVM,U418.6
  2. Research on Autamatic Music Structrue Analysis,TN912.3
  3. Research on Transductive Support Vector Machine and Its Application in Image Retrieval,TP391.41
  4. Fault Diagnosis Method Based on Support Vector Machine,TP18
  5. Process Support Vector Machine and Its Application to Satellite Thermal Equilibrium Temperature Prediction,TP183
  6. Research for Infrared Image Target Identification and Tracking Technology,TP391.41
  7. Study on the Road Condition Monitoring Based on Vehicular 3D Acceleration Sensor,TP274
  8. Research of Diagnosing Cucumber Diseases Based on Hyperspectral Imaging,S436.421
  9. The Research on Intrusion Detection System Based on Machine Learning,TP393.08
  10. Research on Improved K Neighbor Support Vector Machine Algorithm Faced Text Classification,TP391.1
  11. Research on Face Recognition Based on AdaBoost Algorithm,TP391.41
  12. Research on Feature Extraction, Selection and Classification Algorithms for Pulmonary CAD,TP391.41
  13. Research on Subimage Selection and Mathching Method for Synthetic Aperture Radar(SAR) Target Recognition,TN957.52
  14. Research of Facial Expression Recognition Algorithm,TP391.41
  15. Fundus Image Segmentation Based on SVM and Template Matching,TP391.41
  16. The Studies on Some Improvements of the GA and Their Applications in SVM,TP18
  17. The Research and Implementation of the Fault Management System for Triple Play,TP315
  18. Modulation Classification Algorithms of Digital Communication Signals,TN914.3
  19. Capsule endoscopy and endoscopic image portable receiver system bleeding Recognition Algorithm,TP391.41
  20. Fault Diagnosis on Fuel Control System of a Certain Aero-engine,V263.6
  21. Based on self-learning social relation extraction research,TP391.1

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile